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Cerebral Representation of Sound Localization Using Functional Near-Infrared Spectroscopy.

ABSTRACT: Sound localization is an essential part of auditory processing. However, the cortical representation of identifying the direction of sound sources presented in the sound field using functional near-infrared spectroscopy (fNIRS) is currently unknown. Therefore, in this study, we used fNIRS to investigate the cerebral representation of different sound sources. Twenty-five normal-hearing subjects (aged 26 ± 2.7, male 11, female 14) were included and actively took part in a block design task. The test setup for sound localization was composed of a seven-speaker array spanning a horizontal arc of 180° in front of the participants. Pink noise bursts with two intensity levels (48 dB/58 dB) were randomly applied via five loudspeakers (-90°/-30°/-0°/+30°/+90°). Sound localization task performances were collected, and simultaneous signals from auditory processing cortical fields were recorded for analysis by using a support vector machine (SVM). The results showed a classification accuracy of 73.60, 75.60, and 77.40% on average at -90°/0°, 0°/+90°, and -90°/+90° with high intensity, and 70.60, 73.6, and 78.6% with low intensity. The increase of oxyhemoglobin was observed in the bilateral non-primary auditory cortex (AC) and dorsolateral prefrontal cortex (dlPFC). In conclusion, the oxyhemoglobin (oxy-Hb) response showed different neural activity patterns between the lateral and front sources in the AC and dlPFC. Our results may serve as a basic contribution for further research on the use of fNIRS in spatial auditory studies.

SUBMITTER: Tian X

PROVIDER: S-EPMC8712652 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCerebral blood flow (CBF) is maintained over a range of blood pressures through cerebral autoregulation (CA). Blood pressure outside the range of CA, or impaired autoregulation, is associated with adverse patient outcomes. Regional oxygen saturation (rSO2) derived from near-infrared spectroscopy (NIRS) can be used as a surrogate CBF for determining CA, but existing methods require a long period of time to calculate CA metrics. We have developed a novel method to determine CA using cotrending of mean arterial pressure (MAP) with rSO2that aims to provide an indication of CA state within 1 minute. We sought to determine the performance of the cotrending method by comparing its CA metrics to data derived from transcranial Doppler (TCD) methods.MethodsRetrospective data collected from 69 patients undergoing cardiac surgery with cardiopulmonary bypass were used to develop a reference lower limit of CA. TCD-MAP data were plotted to determine the reference lower limit of CA. The investigated method to evaluate CA state is based on the assessment of the instantaneous cotrending relationship between MAP and rSO2 signals. The lower limit of autoregulation (LLA) from the cotrending method was compared to the manual reference derived from TCD. Reliability of the cotrending method was assessed as uptime (defined as the percentage of time that the state of autoregulation could be measured) and time to first post.ResultsThe proposed method demonstrated minimal mean bias (0.22 mmHg) when compared to the TCD reference. The corresponding limits of agreement were found to be 10.79 mmHg (95% confidence interval [CI], 10.09-11.49) and -10.35 mmHg (95% CI, -9.65 to -11.05). Mean uptime was 99.40% (95% CI, 99.34-99.46) and the mean time to first post was 63 seconds (95% CI, 58-71).ConclusionsThe reported cotrending method rapidly provides metrics associated with CA state for patients undergoing cardiac surgery. A major strength of the proposed method is its near real-time feedback on patient CA state, thus allowing for prompt corrective action to be taken by the clinician.

Project description:Continuous monitoring of cerebral autoregulation might provide novel treatment targets and identify therapeutic windows after acute brain injury. Slow oscillations of cerebral hemodynamics (0.05-0.003 Hz) are visible in multimodal neuromonitoring and may be analyzed to provide novel, surrogate measures of autoregulation. Near-infrared spectroscopy (NIRS) is an optical neuromonitoring technique, which shows promise for widespread clinical applicability because it is noninvasive and easily delivered across a wide range of clinical scenarios. The aim of this study is to identify the relationship between NIRS signal oscillations and multimodal neuromonitoring, examining the utility of near infrared derived indices of cerebrovascular reactivity.Twenty-seven sedated, ventilated, brain-injured patients were included in this observational study. Intracranial pressure, transcranial Doppler-derived flow velocity in the middle cerebral artery, and ipsilateral cerebral NIRS variables were continuously monitored. Signals were compared using wavelet measures of phase and coherence to examine the spectral features involved in reactivity index calculations. Established indices of autoregulatory reserve such as the pressure reactivity index (PRx) and mean velocity index (Mx) and the NIRS indices such as total hemoglobin reactivity index (THx) and tissue oxygen reactivity index (TOx) were compared using correlation and Bland-Altman analysis.NIRS indices correlated significantly between PRx and THx (rs = 0.63, P < 0.001), PRx and TOx (r = 0.40, P = 0.04), and Mx and TOx (r = 0.61, P = 0.004) but not between Mx and THx (rs = 0.26, P = 0.28) and demonstrated wide limits between these variables: PRx and THx (bias, -0.06; 95% limits, -0.44 to 0.32) and Mx and TOx (bias, +0.15; 95% limits, -0.34 to 0.64). Analysis of slow-wave activity throughout the intracranial pressure, transcranial Doppler, and NIRS recordings revealed statistically significant interrelationships, which varied dynamically and were nonsignificant at frequencies <0.008 Hz.Although slow-wave activity in intracranial pressure, transcranial Doppler, and NIRS is significantly similar, it varies dynamically in both time and frequency, and this manifests as incomplete agreement between reactivity indices. Analysis informed by a priori knowledge of physiology underpinning NIRS variables combined with sophisticated analysis techniques has the potential to deliver noninvasive surrogate measures of autoregulation, guiding therapy.

Project description:Chronic tinnitus is a debilitating condition which affects 10-20% of adults and can severely impact their quality of life. Currently there is no objective measure of tinnitus that can be used clinically. Clinical assessment of the condition uses subjective feedback from individuals which is not always reliable. We investigated the sensitivity of functional near-infrared spectroscopy (fNIRS) to differentiate individuals with and without tinnitus and to identify fNIRS features associated with subjective ratings of tinnitus severity. We recorded fNIRS signals in the resting state and in response to auditory or visual stimuli from 25 individuals with chronic tinnitus and 21 controls matched for age and hearing loss. Severity of tinnitus was rated using the Tinnitus Handicap Inventory and subjective ratings of tinnitus loudness and annoyance were measured on a visual analogue scale. Following statistical group comparisons, machine learning methods including feature extraction and classification were applied to the fNIRS features to classify patients with tinnitus and controls and differentiate tinnitus at different severity levels. Resting state measures of connectivity between temporal regions and frontal and occipital regions were significantly higher in patients with tinnitus compared to controls. In the tinnitus group, temporal-occipital connectivity showed a significant increase with subject ratings of loudness. Also in this group, both visual and auditory evoked responses were significantly reduced in the visual and auditory regions of interest respectively. Naïve Bayes classifiers were able to classify patients with tinnitus from controls with an accuracy of 78.3%. An accuracy of 87.32% was achieved using Neural Networks to differentiate patients with slight/ mild versus moderate/ severe tinnitus. Our findings show the feasibility of using fNIRS and machine learning to develop an objective measure of tinnitus. Such a measure would greatly benefit clinicians and patients by providing a tool to objectively assess new treatments and patients' treatment progress.

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Cerebral Representation of Sound Localization Using Functional Near-Infrared Spectroscopy.

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